Using autoencoders for training natural language text classifiers

ABSTRACT

Systems and methods for using autoencoders for training natural language classifiers. An example method comprises: producing, by a computer system, a plurality of feature vectors, wherein each feature vector represents a natural language text of a text corpus, wherein the text corpus comprises a first plurality of annotated natural language texts and a second plurality of un-annotated natural language texts; training, using the plurality of feature vectors, an autoencoder represented by an artificial neural network; producing, by the autoencoder, an output of the hidden layer, by processing a training data set comprising the first plurality of annotated natural language texts; and training, using the training data set, a text classifier that accepts an input vector comprising the output of the hidden layer and yields a degree of association, with a certain text category, of a natural language text utilized to produce the output of the hidden layer.

REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/852,418, filed Dec. 22, 2017, which claims the benefit of priorityunder 35 U.S.C. § 119 to Russian Patent Application No. 2017143146 filedDec. 11, 2017. Both above-referenced applications in their respectiveentireties are incorporated by reference herein.

TECHNICAL FIELD

The present disclosure is generally related to computer systems, and ismore specifically related to systems and methods for natural languageprocessing.

BACKGROUND

Various natural language processing tasks may involve classifyingnatural language texts. Examples of such tasks include detectingsemantic similarities, search result ranking, determination of textauthorship, spam filtering, selecting texts for contextual advertising,etc.

SUMMARY OF THE DISCLOSURE

In accordance with one or more aspects of the present disclosure, anexample method of using an autoencoder for training a natural languageclassifier may include: producing, by a computer system, a plurality offeature vectors, wherein each feature vector represents a naturallanguage text of a text corpus, wherein the text corpus comprises afirst plurality of annotated natural language texts and a secondplurality of un-annotated natural language texts; training, using theplurality of feature vectors, an autoencoder represented by anartificial neural network; producing, by the autoencoder, an output ofthe hidden layer, by processing a training data set comprising the firstplurality of annotated natural language texts; and training, using thetraining data set, a text classifier that accepts an input vectorcomprising the output of the hidden layer and yields a degree ofassociation, with a certain text category, of a natural language textutilized to produce the output of the hidden layer.

In accordance with one or more aspects of the present disclosure, anexample system of classifying a natural language text may include amemory and a processor, coupled to the memory, the processor configuredfor: receiving, by a computer system, a natural language text;processing the natural language text by an autoencoder represented by anartificial neural network; feeding, to a text classifier, an inputvector comprising an output of the hidden layer; and determining, usingthe text classifier, a degree of association of the natural languagetext with a certain text category.

In accordance with one or more aspects of the present disclosure, anexample computer-readable non-transitory storage medium may compriseexecutable instructions that, when executed by a computer system, causethe computer system to: produce a plurality of feature vectors, whereineach feature vector represents a natural language text of a text corpus,wherein the text corpus comprises a first plurality of annotated naturallanguage texts and a second plurality of un-annotated natural languagetexts; train, using the plurality of feature vectors, an autoencoderrepresented by an artificial neural network; produce, by theautoencoder, an output of the hidden layer, by processing a trainingdata set comprising the first plurality of annotated natural languagetexts; and train, using the training data set, a text classifier thataccepts an input vector comprising the output of the hidden layer andyields a degree of association, with a certain text category, of anatural language text utilized to produce the output of the hiddenlayer.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of examples, and not by wayof limitation, and may be more fully understood with references to thefollowing detailed description when considered in connection with thefigures, in which:

FIG. 1 schematically illustrates an example workflow employing anautoencoder for training a natural language text classifier, inaccordance with one or more aspects of the present disclosure;

FIG. 2 depicts a flow diagram of one illustrative example of a method ofusing an autoencoder for training a natural language text classifier, inaccordance with one or more aspects of the present disclosure;

FIG. 3 schematically illustrates a structure of an example neuralnetwork operating in accordance with one or more aspects of the presentdisclosure;

FIG. 4 schematically illustrates operation of an example autoencoder, inaccordance with one or more aspects of the present disclosure;

FIG. 5 schematically illustrates a structure of an autoencoder operatingin accordance with one or more aspects of the present disclosure;

FIG. 6 schematically illustrates the output of the hidden layer of theautoencoder processing an example data set, in accordance with one ormore aspects of the present disclosure;

FIG. 7 schematically illustrates the accuracy of text classification bytext classifiers processing concatenated input vectors including thetext features and the autoencoder output, in accordance with one or moreaspects of the present disclosure and text classifiers only processingbags of words;

FIG. 8 depicts a flow diagram of one illustrative example of a methodfor classifying a natural language text, in accordance with one or moreaspects of the present disclosure; and

FIG. 9 depicts a diagram of an example computer system implementing themethods described herein.

DETAILED DESCRIPTION

Described herein are methods and systems for using autoencoders fortraining natural language classifiers. Natural language textclassification may involve associating a given natural language text,which may be represented, e.g., by at least a portion of a document,with one or more categories of a certain set of categories. In certainimplementations, the set of categories may be pre-determined (e.g.,“spam” and “legitimate messages” for classification of electronic mailmessages). Alternatively, the set of categories may be identifiedon-the-fly at the time of performing the classification, by analyzing acorpus of natural language texts, or documents (e.g., multiple items ofa newsfeed).

“Computer system” herein shall refer to a data processing device havinga general purpose processor, a memory, and at least one communicationinterface. Examples of computer systems that may employ the methodsdescribed herein include, without limitation, desktop computers,notebook computers, tablet computers, and smart phones.

In automated text classification, each natural language text may berepresented by a point within a multi-dimensional space of the chosentext features, where the point coordinates are represented by thefeature values. Therefore, performing the text classification mayinvolve determining parameters of one or more separating hyper-planesthat split the multi-dimensional space into sectors representing theclassification categories.

Text classification may be performed by evaluating a classificationfunction, also referred to as classifier, which may be represented by afunction of a plurality of text features that yields the degree ofassociation of the text being classified with a certain category of theplurality of classification categories (e.g., the probability of thetext being associated with a certain category). The text classificationmay involve evaluating a chosen classification function for eachcategory of the plurality of classification categories, and associatingthe natural language text with the category corresponding to the optimal(maximum or minimum) value of the classification function.

In certain implementations, each natural language text may berepresented by a feature vector including a plurality of numericalvalues reflecting the respective text features. In an illustrativeexample, each element of the vector may store a value reflecting certainfrequency characteristics of a word identified by the index of theelement, as described in more detail herein below.

Values of one or more parameters of the classifier may be determined bya supervised learning method, which may involve iteratively modifyingthe parameter values based on analyzing a training data set includingnatural language texts with known classification categories, in order tooptimize a fitness function reflecting the ratio of the number ofnatural language texts of a validation data set that would be classifiedcorrectly using the specified values of the classifier parameters to thetotal number of the natural language texts in the validation data set.

In practice, the number of available annotated texts which may beincluded into the training or validation data set may be relativelysmall, as producing such annotated texts may involve receiving the userinput specifying the classification category for each text. Supervisedlearning based on relatively small training and validation data sets mayproduce poorly performing classifiers.

The present disclosure addresses the above-noted and other deficienciesof known text classification methods by utilizing autoencoders forextracting information from large, mostly un-annotated, text corpuses,such that the extracted information may then be leveraged in theclassifier training process. “Autoencoder” herein shall refer to anartificial neural network employed for unsupervised learning ofencodings of sets of data, typically for the purpose of dimensionalityreduction. An autoencoder may be implemented by a three-layer artificialneural network, in which the dimensions of the input and output vectorsare equal, while the dimension of the hidden intermediate layer issignificantly less than that of the input and output layers, asdescribed in more detail herein below. Unsupervised learning of anautoencoder involves processing a sample data set in order to determinethe values of one or more autoencoder parameters, in order to minimizethe output error reflecting the difference between the input and outputvectors. As the dimension of the hidden layer is significantly less thanthat of the input and output layers, the autoencoder compresses theinput vector by the input layer and then restores is by the outputlayer, thus detecting certain inherent or hidden features of the inputdata set.

FIG. 1 schematically illustrates an example workflow employing anautoencoder for training a natural language text classifier, inaccordance with one or more aspects of the present disclosure. As shownin FIG. 1, an auto-encoder 100 that has been trained on a corpus ofnatural language texts 110 may be employed to process the training dataset represented by an annotated subset 120 of the text corpus 110. Sincethe autoencoder 100 has been trained on the whole corpus 110, the outputof the hidden layer of the autoencoder 100 processing an annotated textfrom the training data set 120 would presumably reflect not only thetext features extracted from an annotated text, but also the informationthat has been gleaned by the autoencoder 100 from the whole text corpus110 during the autoencoder training. Therefore, a classifier 130processing the output of the hidden layer of the autoencoder 100 wouldproduce more accurate results than a classifier directly processing thetext features extracted from the text. The classifier 130 operating onthe output of the hidden layer of the autoencoder 100 may be trainedusing the annotated texts comprised by the training data set 120. Theun-annotated texts of the text corpus 110 and/or other similar texts maythen be classified by a two-stage process which involves employing theautoencoder to produce the output of the hidden layer and then feedingthat output as the input to the trained classifier, as described in moredetail herein below.

Various aspects of the above referenced methods and systems aredescribed in details herein below by way of examples, rather than by wayof limitation.

FIG. 2 depicts a flow diagram of one illustrative example of a method ofusing an autoencoder for training a natural language text classifier, inaccordance with one or more aspects of the present disclosure. Method200 and/or each of its individual functions, routines, subroutines, oroperations may be performed by one or more processors of the computersystem (e.g., computer system 1000 of FIG. 9) implementing the method.In certain implementations, method 200 may be performed by a singleprocessing thread. Alternatively, method 200 may be performed by two ormore processing threads, each thread implementing one or more individualfunctions, routines, subroutines, or operations of the method. In anillustrative example, the processing threads implementing method 200 maybe synchronized (e.g., using semaphores, critical sections, and/or otherthread synchronization mechanisms). Alternatively, the processingthreads implementing method 200 may be executed asynchronously withrespect to each other.

At block 210, a computer system implementing the method may analyze acorpus of natural language texts to produce, for each natural languagetext, a feature vector representing the respective text. The corpus maycomprise texts having a common or similar structure (e.g., news articlesor electronic mail messages) and representing several classificationcategories (e.g., literary works by various persons, spam and legitimateelectronic mail messages, news articles on foreign policy, science, andsports, etc.). A relatively small subset of text corpus may beannotated, i.e., may include texts having a known classificationcategory (such as authorship of a literary work, spam classification ofan electronic mail message, topic of a news article, etc.). It should benoted that methods and systems of the present disclosunammes arewell-suited for processing unbalanced training sets, i.e., the trainingsets in which the number of texts associated with one classificationcategory may be substantially different from the number of textsassociated with another classification category.

The feature vectors representing the respective corpus texts may becombined into a matrix representing the text corpus, such that each rowof the matrix represents a vector of features of a text identified bythe row index, and each column of the matrix represents a certain textfeature, e.g., an occurrence of a word identified by the column index.

In an illustrative example, each text may be represented by a “bag ofwords,” i.e., an unordered or arbitrarily ordered set of words containedby the text. Therefore, each matrix cell may store an integer valuereflecting the number of occurrences, in the document identified by therow, of the word associated with the column.

In order to reduce the level of noise which may be caused by certainfrequently occurring words which do not determine the document category(e.g., articles, prepositions, auxiliary verbs, etc.), each naturallanguage text may be represented by a vector of term frequency—inversedocument frequency (TF-IDF) values.

Term frequency (TF) represents the frequency of occurrence of a givenword in the document:

tf(t,d)=n _(t) /Σn _(k)

where t is the word identifier,

d is the document identifier,

n_(t) is the number of occurrences of the word t within document d, and

Σn_(k) is the total number of words within document d.

Inverse document frequency (IDF) is defined as the logarithmic ratio ofthe number of texts in the corpus to the number of documents containingthe given word:

idf(t, D)=log[|D|/{diϵD|tϵdi}/]

where D is the text corpus identifier,

|D| is the number of documents in the corpus, and

{di ∈D|t ∈di} is the number of documents of the corpus D which containthe word t.

Thus, TF-IDF may be defined as the product the product of the termfrequency (TF) and the inverse document frequency (IDF):

tf-idf(t, d, D)=tf (t, d)*idf (t, D)

TF-IDF would produce larger values for words that are more frequentlyoccurring in one document that on other documents of the corpus.Accordingly, the text corpus may be represented by a matrix, each cellof which stores the TF-IDF value of the word identified by the columnindex in the document identified by the row index.

In various alternative implementations, other types of features whichmay be extracted from natural language texts, including morphological,syntactical, and/or semantic features, may be utilized for textclassification by the systems and methods of the present disclosure, inaddition to, or instead of the above-described TF-IDF values.

At block 220, the computer system may utilize the feature vectorsrepresenting the natural language texts to perform unsupervised learningof an autoencoder, which will then be employed for producing theclassifier input. In an illustrative example, the autoencoder may berepresented by a three-layer artificial neural network.

A neural network is a computational model based on a multi-stagedalgorithm that applies a set of pre-defined functional transformationsto a plurality of inputs (e.g., a feature vector representing adocument) and then utilizes the transformed data for informationextraction, pattern recognition, etc. In an illustrative example, aneural network may include multiple artificial neurons, which receiveinput, change their internal state according to that input and anactivation function, and produce output depending on the input and theactivated internal state. A neural network may be formed by connectingthe output of certain neurons to the input of other neurons to form adirected weighted graph, in which the neurons represent the nodes andthe connection between the neurons represent weighted directed edges.The weights and the activation function parameters can be modified by alearning process.

FIG. 3 schematically illustrates a structure of an example neuralnetwork operating in accordance with one or more aspects of the presentdisclosure. As shown in FIG. 3, the neural network 300 may include theinput layer 310, the hidden layer 320, and the output layer 330. Theinput layer 310 may include one or more neurons 340A-340N, which may beconnected to one or more neurons 350A-350K of the hidden layer 320. Thehidden layer neurons 350A-350K may, in turn, be connected to one or moreneurons 360 of the output layer 330.

As noted herein above, a three-layer artificial neural network, in whichthe dimensions of the input and output vectors are equal, while thedimension of the hidden intermediate layer is significantly less thanthat of the input and output layers, may implement an autoencoder, whichmay be employed for unsupervised learning of encodings of sets of data,typically for the purpose of dimensionality reduction.

FIG. 4 schematically illustrates operation of an example autoencoder, inaccordance with one or more aspects of the present disclosure. As shownin FIG. 4, the example autoencoder 400 may include an encoder stage 410and a decoder stage 420. The encoder stage 410 of the autoencoder mayreceive the input vector x and map it to the latent representation z,and the dimension of which is significantly less than that of the inputvector:

z−σ(Wx+b),

where σ is the activation function, which may be represented by asigmoid function or by a rectifier linear unit,

W is the weight matrix, and

b is the bias vector.

The decoder stage 420 of the autoencoder may map the latentrepresentation z to the reconstruction vector x′ having the samedimension as the input vector x:

X′−σ′(W′z+b′).

The autoencoder may be trained to minimize the reconstruction error:

L(x, x′)=∥x−x′∥² =∥x−σ′(σ(Wx+b))+b′)∥²,

where x may be averaged over the training data set.

As the dimension of the hidden layer is significantly less than that ofthe input and output layers, the autoencoder compresses the input vectorby the input layer and then restores is by the output layer, thusdetecting certain inherent or hidden features of the input data set.

FIG. 5 schematically illustrates a structure of an example autoencoderoperating in accordance with one or more aspects of the presentdisclosure. As shown in FIG. 5, the autoencoder 500 may be representedby a feed-forward, non-recurrent neural network including an input layer510, an output layer 520 and one or more hidden layers 530 connectingthe input layer 510 and the output layer 520. The output layer 520 mayhave the same number of nodes as the input layer 510, such that thenetwork 500 may be trained, by an unsupervised learning process, toreconstruct its own inputs.

In certain implementations, the activation function of the hidden layerof the autoencoder may be represented by a rectified linear unit (ReLU),which may be described by the following formula:

σ(x)=max(0, x).

In certain implementations, the activation function of the output layerof the autoencoder may be represented by a rectified linear unit (ReLU),which may be described by the following formula:

σ(x)=1/(1+e ^(−x)).

Unsupervised learning of the autoencoder may involve, for each inputvector x, performing a feed-forward pass to obtain the output x′,measuring the output error reflected by the loss function L(x, x′), andback-propagating the output error through the network to update thedimension of the hidden layer, the weights, and/or activation functionparameters. In an illustrative example, the loss function may berepresented by the binary cross-entropy function. The training processmay be repeated until the output error is below a predeterminedthreshold.

Referring again to FIG. 2, at block 230, the computer system may splitthe annotated subset of the text corpus into the training data set andvalidation data set. In certain implementations, a k-foldcross-validation method may be applied to the corpus of natural languagetexts. The method may involve randomly partitioning the annotated textsinto k equal sized subsets, one of which is then utilized as thevalidation data set, and the remaining k−1 compose the training dataset. The cross-validation process may then be repeated k times, so thateach of the k subsets would once be used as the validation data set.

At block 240, the computer system may utilize the trained autoencoder toprocess the identified training data set in order to produce the outputof the autoencoder's hidden layer. Since the autoencoder has beentrained on the whole corpus of texts including both un-annotated andannotated texts, the output of the hidden layer of the autoencoderprocessing an annotated text from the training data set would presumablyreflect not only the input text features of the particular annotatedtext, but also the information that has been gleaned by the autoencoderfrom the whole text corpus during the autoencoder training.

At block 250, the computer system may train the classifier utilizing theoutput produced by the hidden layer of the autoencoder as the input ofthe classifier. In certain implementations, the classifier may berepresented by a linear Support Vector Classification (LinearSVC)classifier. Training the classifier may involve iteratively identify thevalues of certain parameters of the text classifier model that wouldoptimize a chosen fitness function. In an illustrative example, thefitness function may reflect the number of natural language texts of thevalidation data set that would be classified correctly using thespecified values of the classifier parameters. In certainimplementations, the fitness function may be represented by the F-score,which is defined as the weighted harmonic mean of the precision andrecall of the test:

F−2*P*R/(P+R),

where P is the number of correct positive results divided by the numberof all positive results, and

R is the number of correct positive results divided by the number ofpositive results that should have been returned.

At block 260, the computer system may utilize the trained classifier toperform a natural language processing task. Examples natural languageprocessing tasks include detecting semantic similarities, search resultranking, determination of text authorship, spam filtering, selectingtexts for contextual advertising, etc. Upon completing the operations ofblock 260, the method may terminate.

In an illustrative example, the trained classifier may be employed forclassifying the un-annotated texts of the text corpus 110 and/or othersimilar texts. The classification process may involve employing theautoencoder to produce the output of the hidden layer, and then feedingthat output to the trained classifier. The text classification mayinvolve evaluating a chosen classification function for each category ofthe plurality of classification categories, and associating the naturallanguage text with the category corresponding to the optimal (maximum orminimum) value of the classification function, as described in moredetail herein above, as described in more detail herein below withreferences to FIG. 7.

For relatively small training data sets, classifiers trained on theautoencoder output may provide better accuracy than classifiers directlyprocessing the features extracted from an annotated text. FIG. 6schematically illustrates the output of the hidden layer of theautoencoder processing an example data set. Each plotted shaperepresents a natural language text, such that the texts classified tothe same category are represented by shapes of the same type. As shownby FIG. 6, the output of the hidden layer of the autoencoder exhibitsreadily perceivable clusterization even after having been transformed,by reducing the number of independent coordinates from the number equalto the dimension of the hidden layer of the autoencoder to twoindependent coordinates, for performing the two-dimensionalvisualization.

In practice, a text corpus may initially include only a small subset ofannotated documents, but their number may increase with new documentsbeing received, classified, and their classification validated (e.g., bysoliciting and receiving a user interface input confirming or modifyingthe document category produced by a text classifier). Thus, in certainimplementations, for certain text corpuses, the output of the hiddenlayer may be concatenated with the feature vector extracted from thenatural language text, and the resulting concatenated vector may be fedto the classifier input for training the classifier.

FIG. 7 schematically illustrates the accuracy of text classification bytext classifiers processing concatenated input vectors including thetext features and the autoencoder output and text classifiers onlyprocessing bags of words. As shown in FIG. 7, the accuracy of theexample text classifier 710 which processes concatenated input vectorsincluding the text features and the autoencoder output exceeds, onsmaller sizes of training data sets, both the accuracy of a linearclassifier 720 only processing bags of words and the accuracy of arandom forest classifier 730.

FIG. 8 depicts a flow diagram of one illustrative example of a methodfor classifying a natural language text, in accordance with one or moreaspects of the present disclosure. Method 800 and/or each of itsindividual functions, routines, subroutines, or operations may beperformed by one or more processors of the computer system (e.g.,computer system 1000 of FIG. 9) implementing the method. In certainimplementations, method 800 may be performed by a single processingthread. Alternatively, method 800 may be performed by two or moreprocessing threads, each thread implementing one or more individualfunctions, routines, subroutines, or operations of the method. In anillustrative example, the processing threads implementing method 800 maybe synchronized (e.g., using semaphores, critical sections, and/or otherthread synchronization mechanisms). Alternatively, the processingthreads implementing method 800 may be executed asynchronously withrespect to each other.

At block 810, a computer system implementing the method may receive anatural language text to be classified by associating with a category ofa pre-determined set of categories.

At block 820, the computer system may employ an autoencoder, which hasbeen pre-trained on a large text corpus, to process the received naturallanguage text and produce the output of the autoencoder's hidden layer,as described in more detail herein above.

At block 830, the computer system may feed the output of the hiddenlayer of the autoencoder to one or more classifiers, which has beenpre-trained on an annotated subset of the text corpus, as described inmore detail herein above.

At block 840, each classifier may produce the degree of association ofthe text being classified with a respective category of the plurality ofclassification categories, as described in more detail herein above.

At block 850, the computer system may select the optimal (e.g., maximalor minimal) value among the values produced by the classifiers, andassociate the natural language text with the category corresponding tothe classifier that has produced the selected optimal value.

At block 860, the computer system may utilize the identified textcategory to perform a natural language processing task. Examples naturallanguage processing tasks include detecting semantic similarities,search result ranking, determination of text authorship, spam filtering,selecting texts for contextual advertising, etc. Upon completing theoperations of block 280, the method may terminate.

FIG. 9 illustrates a diagram of an example computer system 1000 whichmay execute a set of instructions for causing the computer system toperform any one or more of the methods discussed herein. The computersystem may be connected to other computer system in a LAN, an intranet,an extranet, or the Internet. The computer system may operate in thecapacity of a server or a client computer system in client-servernetwork environment, or as a peer computer system in a peer-to-peer (ordistributed) network environment. The computer system may be a providedby a personal computer (PC), a tablet PC, a set-top box (STB), aPersonal Digital Assistant (PDA), a cellular telephone, or any computersystem capable of executing a set of instructions (sequential orotherwise) that specify operations to be performed by that computersystem. Further, while only a single computer system is illustrated, theterm “computer system” shall also be taken to include any collection ofcomputer systems that individually or jointly execute a set (or multiplesets) of instructions to perform any one or more of the methodologiesdiscussed herein.

Exemplary computer system 1000 includes a processor 902, a main memory904 (e.g., read-only memory (ROM) or dynamic random access memory(DRAM)), and a data storage device 918, which communicate with eachother via a bus 930.

Processor 902 may be represented by one or more general-purpose computersystems such as a microprocessor, central processing unit, or the like.More particularly, processor 902 may be a complex instruction setcomputing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,or a processor implementing other instruction sets or processorsimplementing a combination of instruction sets. Processor 902 may alsobe one or more special-purpose computer systems such as an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA), a digital signal processor (DSP), network processor, or thelike. Processor 902 is configured to execute instructions 926 forperforming the operations and functions discussed herein.

Computer system 1000 may further include a network interface device 922,a video display unit 910, a character input device 912 (e.g., akeyboard), and a touch screen input device 914.

Data storage device 918 may include a computer-readable storage medium924 on which is stored one or more sets of instructions 926 embodyingany one or more of the methodologies or functions described herein.Instructions 926 may also reside, completely or at least partially,within main memory 904 and/or within processor 902 during executionthereof by computer system 1000, main memory 904 and processor 902 alsoconstituting computer-readable storage media. Instructions 926 mayfurther be transmitted or received over network 916 via networkinterface device 922.

In certain implementations, instructions 926 may include instructions ofmethods 200, 700 for training a text classifier and classifying naturallanguage texts, in accordance with one or more aspects of the presentdisclosure. While computer-readable storage medium 924 is shown in theexample of FIG. 9 to be a single medium, the term “computer-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterm “computer-readable storage medium” shall also be taken to includeany medium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present disclosure.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

The methods, components, and features described herein may beimplemented by discrete hardware components or may be integrated in thefunctionality of other hardware components such as ASICS, FPGAs, DSPs orsimilar devices. In addition, the methods, components, and features maybe implemented by firmware modules or functional circuitry withinhardware devices. Further, the methods, components, and features may beimplemented in any combination of hardware devices and softwarecomponents, or only in software.

In the foregoing description, numerous details are set forth. It will beapparent, however, to one of ordinary skill in the art having thebenefit of this disclosure, that the present disclosure may be practicedwithout these specific details. In some instances, well-known structuresand devices are shown in block diagram form, rather than in detail, inorder to avoid obscuring the present disclosure.

Some portions of the detailed description have been presented in termsof algorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “determining,” “computing,” “calculating,” “obtaining,”“identifying,” “modifying” or the like, refer to the actions andprocesses of a computer system, or similar electronic computer system,that manipulates and transforms data represented as physical (e.g.,electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but not limited to, any type of diskincluding floppy disks, optical disks, CD-ROMs, and magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, or any type of media suitable forstoring electronic instructions.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Various other implementations will beapparent to those of skill in the art upon reading and understanding theabove description. The scope of the disclosure should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A method, comprising: receiving, by a computersystem, a natural language text; processing the natural language text byan autoencoder represented by an artificial neural network comprising aninput layer, a hidden layer, and an output layer; feeding, to a textclassifier, an input vector comprising an output of the hidden layer;and determining, using the text classifier, a degree of association ofthe natural language text with a certain text category.
 2. The method ofclaim 1, wherein the hidden layer comprises an activation functionprovided by a rectified linear unit.
 3. The method of claim 1, wherein afirst dimension of the input layer is equal to a second dimension of theoutput layer and is greater than a third dimension of the hidden layer.4. The method of claim 1, further comprising: producing a feature vectorcomprising a plurality of term frequency-inverse document frequency(TF-IDF) values, each value reflecting a frequency of occurrence, in thenatural language text, of a word identified by an index of the value inthe feature vector; and producing the input vector by concatenating theoutput of the hidden layer and the feature vector.
 5. The method ofclaim 1, further comprising: producing a plurality of classifier inputvectors, wherein each classifier input vector comprises an output of thehidden layer; and training the text classifier using the plurality ofclassifier input vectors, wherein each classifier input vector isassociated with a known category of a training natural language textthat has been utilized for producing the output of the hidden layer. 6.The method of claim 1, further comprising: producing a plurality ofclassifier input vectors, wherein each classifier input vector comprisesa combination of a feature vector representing a natural language textand an output of the hidden layer produced by processing the naturallanguage text; and training the text classifier using the plurality ofclassifier input vectors, wherein each classifier input vector isassociated with a known category of a training natural language textthat has been utilized for producing the output of the hidden layer. 7.The method of claim 1, further comprising: producing a plurality offeature vectors, wherein each feature vector represents a trainingnatural language text of a text corpus, wherein the text corpuscomprises a first plurality of annotated natural language texts and asecond plurality of un-annotated natural language texts; training theautoencoder using the plurality of feature vectors.
 8. The method ofclaim 1, further comprising: performing, based on the degree ofassociation, a natural language processing task.
 9. A system,comprising: a memory; a processor, coupled to the memory, the processorconfigured to: receive a natural language text; process the naturallanguage text by an autoencoder represented by an artificial neuralnetwork comprising an input layer, a hidden layer, and an output layer;feed, to a text classifier, an input vector comprising an output of thehidden layer; and determine, using the text classifier, a degree ofassociation of the natural language text with a certain text category.10. The system of claim 9, wherein the hidden layer comprtises anactivation function provided by a rectified linear unit.
 11. The systemof claim 9, wherein a first dimension of the input layer is equal to asecond dimension of the output layer and is greater than a thirddimension of the hidden layer.
 12. The system of claim 9, wherein theprocessor is further configured to: produce a feature vector comprisinga plurality of term frequency-inverse document frequency (TF-IDF)values, each value reflecting a frequency of occurrence, in the naturallanguage text, of a word identified by an index of the value in thefeature vector; and producing the input vector by concatenating theoutput of the hidden layer and the feature vector.
 13. The system ofclaim 9, wherein the processor is further configured to: produce aplurality of classifier input vectors, wherein each classifier inputvector comprises an output of the hidden layer; and train the textclassifier using the plurality of classifier input vectors, wherein eachclassifier input vector is associated with a known category of atraining natural language text that has been utilized for producing theoutput of the hidden layer.
 14. The system of claim 9, wherein theprocessor is further configured to: produce a plurality of classifierinput vectors, wherein each classifier input vector comprises acombination of a feature vector representing a natural language text andan output of the hidden layer produced by processing the naturallanguage text; and training the text classifier using the plurality ofclassifier input vectors, wherein each classifier input vector isassociated with a known category of a training natural language textthat has been utilized for producing the output of the hidden layer. 15.The system of claim 9, wherein the processor is further configured to:produce a plurality of feature vectors, wherein each feature vectorrepresents a training natural language text of a text corpus, whereinthe text corpus comprises a first plurality of annotated naturallanguage texts and a second plurality of un-annotated natural languagetexts; training the autoencoder using the plurality of feature vectors.16. The system of claim 9, wherein the processor is further configuredto: perform, based on the degree of association, a natural languageprocessing task.
 17. A non-transitory computer-readable storage mediumcomprising executable instructions that, when executed by a computersystem, cause the computer system to: receive a natural language text;process the natural language text by an autoencoder represented by anartificial neural network comprising an input layer, a hidden layer, andan output layer; feed, to a text classifier, an input vector comprisingan output of the hidden layer; and determine, using the text classifier,a degree of association of the natural language text with a certain textcategory.
 18. The non-transitory computer-readable storage medium ofclaim 17, further comprising executable instructions that, when executedby the computer system, cause the computer system to: perform, based onthe degree of association, a natural language processing task.
 19. Thenon-transitory computer-readable storage medium of claim 17, wherein afirst dimension of the input layer is equal to a second dimension of theoutput layer and is greater than a third dimension of the hidden layer.20. The non-transitory computer-readable storage medium of claim 17,further comprising executable instructions that, when executed by thecomputer system, cause the computer system to: produce a feature vectorcomprising a plurality of term frequency-inverse document frequency(TF-IDF) values, each value reflecting a frequency of occurrence, in thenatural language text, of a word identified by an index of the value inthe feature vector; and producing the input vector by concatenating theoutput of the hidden layer and the feature vector.